AI Media Automation Portal is an end-to-end AI-powered content automation platform designed to streamline the full media creation lifecycle — from content discovery and script generation to video creation, human approval, YouTube publishing, and performance tracking.
Manual content creation is repetitive
Manual content creation requires multiple repetitive steps:
Searching for trending topics
Reviewing multiple websites
Selecting the best topic
Writing YouTube-friendly scripts
Creating voiceover
Finding visuals and B-roll
Rendering video
Uploading to YouTube
Tracking performance metrics
The platform solves this by creating a configurable AI workflow where content generation can be automated, reviewed, and published from one centralized dashboard.
What the platform delivers
Automate topic-based content discovery
Region-specific selection: Global, Middle East, South Asia, UAE, India
Generate AI-ranked top content ideas
Enable Human-in-the-Loop dashboard approvals
Generate short-form YouTube scripts
Support script regeneration and AI-assisted improvement
Generate video assets and final video output
Upload approved videos to YouTube
Track views, likes, comments, and engagement
Maintain dropdowns and sources from backend master tables
Solution Lead & Architect
Nikhil designed the overall solution architecture, user journey, backend data model, automation orchestration approach, Human-in-the-Loop approval model, Supabase backend structure, LOV-based master data design, Lovable frontend pages, n8n workflow orchestration, AI prompts, YouTube upload process, and scalable multi-topic / multi-region architecture.
The tools behind the platform
Frontend
Lovable
Backend Database
Supabase PostgreSQL
Authentication
Supabase Auth
Storage
Supabase Storage
Workflow Automation
n8n
AI Orchestration
n8n AI workflows
AI Capabilities
Prompt engineering, scoring, scripts, refinement
Content Sources
RSS feeds, websites, scraping sources
Video Workflow
AI voice, captions, B-roll, rendering
Publishing
YouTube Data API
Analytics
YouTube Analytics / Data API
Governance
Human-in-the-Loop checkpoints
Three major layers
Frontend Layer
Lovable
- Login page
- Setup page
- Ideas dashboard
- Script review page
- Video preview page
- YouTube metrics dashboard
- Admin master data page
Backend Layer
Supabase
- User authentication
- Master data
- LOV dropdown values
- Topic and region website sources
- User configurations
- Generated content ideas
- Scripts and script versions
- Video records
- YouTube upload records
- Metrics
- Workflow logs
Automation Layer
n8n
- Scheduled execution
- Manual Run Now
- Website scraping
- AI content scoring
- Top 3 idea generation
- Script generation
- Script regeneration
- Script improvement
- Video generation
- YouTube upload
- Metrics sync
- Workflow logging
One dashboard to configure the pipeline
Topic
Region
Frequency
Generation Time
Language
Tone
Video Duration
YouTube Visibility
Website Sources
Backend-driven dropdowns
All dropdowns are maintained in Supabase backend tables and are not hardcoded in the frontend. This makes the platform flexible and scalable.
lov_topicslov_regionslov_frequencieslov_languageslov_toneslov_video_durationslov_youtube_visibilitytopic_region_sourcesApproval checkpoints at every stage
Idea Review
Approve one idea or reject all
Script Review
Approve, regenerate, or edit and improve
Video Preview
Approve upload or request changes
Publishing
Upload approved video to YouTube
Orchestrated workflow nodes
Generate Topic Sources
01Run Now
02Scheduled Runner
03Scrape and Score Ideas
04Approve Idea
05Reject All Ideas
06Generate Script
07Regenerate Script
08Improve Script
09Approve Script
10Generate Video
11Approve Upload
12Sync Metrics
13From login to live metrics
User logs into Lovable portal
User selects topic and region
Supabase returns website sources
User saves configuration
n8n runs workflow
n8n scrapes selected websites
AI scores articles
Top 3 ideas are stored in Supabase
User approves one idea
AI generates YouTube script
User reviews script
AI generates video
User previews video
User approves upload
n8n uploads to YouTube
Metrics sync back to Supabase
The applied AI stack
Agentic Workflow Orchestration
Coordinated AI steps run discovery, scoring, scripting, and publishing across n8n.
Prompt Engineering
Structured prompts control scoring criteria, script tone, and output formatting.
AI-Based Content Scoring
Models rank scraped articles by relevance and engagement potential.
Generative AI Script Creation
Short-form YouTube scripts generated from the approved idea.
Human-in-the-Loop Governance
Approval checkpoints at every critical decision before publishing.
AI-Assisted Content Refinement
Scripts can be regenerated and improved with AI suggestions.
Performance Feedback Loop
YouTube metrics loop back into Supabase to inform future scoring.
Measurable outcomes
Reduced manual effort in content research
Faster content idea discovery
Consistent script quality
Better governance through approval checkpoints
Centralized content operations dashboard
Scalable topic and region configuration
Reusable AI workflow architecture
Better visibility into YouTube performance
This project demonstrates how AI can be used beyond simple chatbot use cases and applied to a real operational workflow — combining generative AI, agentic automation, Human-in-the-Loop governance, workflow orchestration, backend-driven configuration, media automation, and performance analytics.